# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Keras 1D convolution layer."""
# pylint: disable=g-classes-have-attributes,g-direct-tensorflow-import

from keras import activations
from keras import constraints
from keras import initializers
from keras import regularizers
from keras.layers.convolutional.base_conv import Conv

from tensorflow.python.util.tf_export import keras_export


@keras_export('keras.layers.Conv1D', 'keras.layers.Convolution1D')
class Conv1D(Conv):
  """1D convolution layer (e.g. temporal convolution).

  This layer creates a convolution kernel that is convolved
  with the layer input over a single spatial (or temporal) dimension
  to produce a tensor of outputs.
  If `use_bias` is True, a bias vector is created and added to the outputs.
  Finally, if `activation` is not `None`,
  it is applied to the outputs as well.

  When using this layer as the first layer in a model,
  provide an `input_shape` argument
  (tuple of integers or `None`, e.g.
  `(10, 128)` for sequences of 10 vectors of 128-dimensional vectors,
  or `(None, 128)` for variable-length sequences of 128-dimensional vectors.

  Examples:

  >>> # The inputs are 128-length vectors with 10 timesteps, and the batch size
  >>> # is 4.
  >>> input_shape = (4, 10, 128)
  >>> x = tf.random.normal(input_shape)
  >>> y = tf.keras.layers.Conv1D(
  ... 32, 3, activation='relu',input_shape=input_shape[1:])(x)
  >>> print(y.shape)
  (4, 8, 32)

  >>> # With extended batch shape [4, 7] (e.g. weather data where batch
  >>> # dimensions correspond to spatial location and the third dimension
  >>> # corresponds to time.)
  >>> input_shape = (4, 7, 10, 128)
  >>> x = tf.random.normal(input_shape)
  >>> y = tf.keras.layers.Conv1D(
  ... 32, 3, activation='relu', input_shape=input_shape[2:])(x)
  >>> print(y.shape)
  (4, 7, 8, 32)

  Args:
    filters: Integer, the dimensionality of the output space
      (i.e. the number of output filters in the convolution).
    kernel_size: An integer or tuple/list of a single integer,
      specifying the length of the 1D convolution window.
    strides: An integer or tuple/list of a single integer,
      specifying the stride length of the convolution.
      Specifying any stride value != 1 is incompatible with specifying
      any `dilation_rate` value != 1.
    padding: One of `"valid"`, `"same"` or `"causal"` (case-insensitive).
      `"valid"` means no padding. `"same"` results in padding with zeros evenly
      to the left/right or up/down of the input such that output has the same
      height/width dimension as the input.
      `"causal"` results in causal (dilated) convolutions, e.g. `output[t]`
      does not depend on `input[t+1:]`. Useful when modeling temporal data
      where the model should not violate the temporal order.
      See [WaveNet: A Generative Model for Raw Audio, section
        2.1](https://arxiv.org/abs/1609.03499).
    data_format: A string,
      one of `channels_last` (default) or `channels_first`.
    dilation_rate: an integer or tuple/list of a single integer, specifying
      the dilation rate to use for dilated convolution.
      Currently, specifying any `dilation_rate` value != 1 is
      incompatible with specifying any `strides` value != 1.
    groups: A positive integer specifying the number of groups in which the
      input is split along the channel axis. Each group is convolved
      separately with `filters / groups` filters. The output is the
      concatenation of all the `groups` results along the channel axis.
      Input channels and `filters` must both be divisible by `groups`.
    activation: Activation function to use.
      If you don't specify anything, no activation is applied
      (see `keras.activations`).
    use_bias: Boolean, whether the layer uses a bias vector.
    kernel_initializer: Initializer for the `kernel` weights matrix
      (see `keras.initializers`). Defaults to 'glorot_uniform'.
    bias_initializer: Initializer for the bias vector
      (see `keras.initializers`). Defaults to 'zeros'.
    kernel_regularizer: Regularizer function applied to
      the `kernel` weights matrix (see `keras.regularizers`).
    bias_regularizer: Regularizer function applied to the bias vector
      (see `keras.regularizers`).
    activity_regularizer: Regularizer function applied to
      the output of the layer (its "activation")
      (see `keras.regularizers`).
    kernel_constraint: Constraint function applied to the kernel matrix
      (see `keras.constraints`).
    bias_constraint: Constraint function applied to the bias vector
      (see `keras.constraints`).

  Input shape:
    3+D tensor with shape: `batch_shape + (steps, input_dim)`

  Output shape:
    3+D tensor with shape: `batch_shape + (new_steps, filters)`
      `steps` value might have changed due to padding or strides.

  Returns:
    A tensor of rank 3 representing
    `activation(conv1d(inputs, kernel) + bias)`.

  Raises:
    ValueError: when both `strides > 1` and `dilation_rate > 1`.
  """

  def __init__(self,
               filters,
               kernel_size,
               strides=1,
               padding='valid',
               data_format='channels_last',
               dilation_rate=1,
               groups=1,
               activation=None,
               use_bias=True,
               kernel_initializer='glorot_uniform',
               bias_initializer='zeros',
               kernel_regularizer=None,
               bias_regularizer=None,
               activity_regularizer=None,
               kernel_constraint=None,
               bias_constraint=None,
               **kwargs):
    super(Conv1D, self).__init__(
        rank=1,
        filters=filters,
        kernel_size=kernel_size,
        strides=strides,
        padding=padding,
        data_format=data_format,
        dilation_rate=dilation_rate,
        groups=groups,
        activation=activations.get(activation),
        use_bias=use_bias,
        kernel_initializer=initializers.get(kernel_initializer),
        bias_initializer=initializers.get(bias_initializer),
        kernel_regularizer=regularizers.get(kernel_regularizer),
        bias_regularizer=regularizers.get(bias_regularizer),
        activity_regularizer=regularizers.get(activity_regularizer),
        kernel_constraint=constraints.get(kernel_constraint),
        bias_constraint=constraints.get(bias_constraint),
        **kwargs)

# Alias

Convolution1D = Conv1D
